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16 November 2020

Publication details

T. Cucinotta, G. Lanciano, A. Ritacco, M. Vannucci, A. Artale, J. Barata, E. Sposato, L. Basili. "Behavioral analysis for Virtualized Network Functions: a SOM-based approach," in Proceedings of the 10th International Conference on Cloud Computing and Services Science (CLOSER 2020), May 7-9, 2020, Prague, Czech Republic.

Abstract

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for net- work function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource con- sumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions.

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Extended version

G. Lanciano, A. Ritacco, F. Brau, T. Cucinotta, M. Vannucci, A. Artale, J. Barata, E. Sposato. "Using Self-Organizing Maps for the Behavioral Analysis of Virtualized Network Functions," in 10th International Conference on Cloud Computing and Services Science (CLOSER 2020), Communications in Computer and Information Science (CCIS), Vol. 1399, Springer, Cham.

Abstract

Detecting anomalous behaviors in a network function virtu- alization infrastructure is of the utmost importance for network operators. In this paper, we propose a technique, based on Self-Organizing Maps, to address such problem by leveraging on the massive amount of historical system data that is typically available in these infrastructures. Indeed, our method consists of a joint analysis of system-level metrics, provided by the virtualized infrastructure monitoring system and referring to resource consumption patterns of the physical hosts and the virtual machines (or containers) that run on top of them, and application-level metrics, provided by the individual virtualized network functions monitoring subsystems and related to the performance levels of the individual applications. The implementation of our approach has been validated on real data coming from a subset of the Vodafone infrastructure for network function virtualization, where it is currently employed to support the decisions of data center operators. Experimental results show that our technique is capable of identifying specific points in space (i.e., components of the infrastructure) and time of the recent evolution of the monitored infrastructure that are worth to be investigated by human operators in order to keep the system running under expected conditions.

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